Processing data access transactions in a dispersed storage network using source revision indicators

ABSTRACT

A method by a dispersed storage (DS) processing unit of a dispersed storage network (DSN) begins by sending a set of data access requests regarding a data access transaction to a set of storage units of the DSN. The method continues by receiving from each of at least some storage units, a storage-revision indicator which includes a content-revision field, a delete-counter field, and a contest-counter field. The method continues by generating an anticipated storage-revision indicator based on a current revision level of the set of encoded data slices and based on a data access type of the data access transaction. The method continues by comparing the anticipated storage-revision indicator with the storage-revision indicators. When a threshold number of the storage-revision indicators received from the at least some storage units substantially match the anticipated storage-revision indicator, the method continues by executing the data access transaction.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not applicable.

INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC

Not applicable.

BACKGROUND OF THE INVENTION Technical Field of the Invention

This invention relates generally to computer networks and moreparticularly to dispersing error encoded data.

Description of Related Art

Computing devices are known to communicate data, process data, and/orstore data. Such computing devices range from wireless smart phones,laptops, tablets, personal computers (PC), work stations, and video gamedevices, to data centers that support millions of web searches, stocktrades, or on-line purchases every day. In general, a computing deviceincludes a central processing unit (CPU), a memory system, userinput/output interfaces, peripheral device interfaces, and aninterconnecting bus structure.

As is further known, a computer may effectively extend its CPU by using“cloud computing” to perform one or more computing functions (e.g., aservice, an application, an algorithm, an arithmetic logic function,etc.) on behalf of the computer. Further, for large services,applications, and/or functions, cloud computing may be performed bymultiple cloud computing resources in a distributed manner to improvethe response time for completion of the service, application, and/orfunction. For example, Hadoop is an open source software framework thatsupports distributed applications enabling application execution bythousands of computers.

In addition to cloud computing, a computer may use “cloud storage” aspart of its memory system. As is known, cloud storage enables a user,via its computer, to store files, applications, etc. on an Internetstorage system. The Internet storage system may include a RAID(redundant array of independent disks) system and/or a dispersed storagesystem that uses an error correction scheme to encode data for storage.

Distributed storage systems often utilize a three-phase process forwriting consistently in a dispersed storage network (DSN) memory, wherethe three phases include: (1) A write phase; (2) A commit phase; and (3)A finalize phase. The three phases address consistency issues that mayarise from different storage units of the DSN holding differentrevisions of encoded data slices, where data is dispersed storage errorencoded to produce the encoded data slices. The three phases are knownto utilize a threshold approach to advance the writing process to thenext phase or to reverse the process when conflicts and errors arise tomaintain consistency of revision storage.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

FIG. 1 is a schematic block diagram of an embodiment of a dispersed ordistributed storage network (DSN) in accordance with the presentinvention;

FIG. 2 is a schematic block diagram of an embodiment of a computing corein accordance with the present invention;

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data in accordance with the present invention;

FIG. 4 is a schematic block diagram of a generic example of an errorencoding function in accordance with the present invention;

FIG. 5 is a schematic block diagram of a specific example of an errorencoding function in accordance with the present invention;

FIG. 6 is a schematic block diagram of an example of a slice name of anencoded data slice (EDS) in accordance with the present invention;

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of data in accordance with the present invention;

FIG. 8 is a schematic block diagram of a generic example of an errordecoding function in accordance with the present invention;

FIGS. 9A and 9B are schematic block diagrams of examples of a storagerevision indicator and an anticipated storage revision indicator inaccordance with the present invention;

FIG. 10 is a schematic block diagram of an example of overlapping writerequests and read requests for a set of encoded data slices inaccordance with the present invention;

FIG. 11 is a schematic block diagram of an example of a write request inaccordance with the present invention;

FIG. 12 is a schematic block diagram of an example of a read request inaccordance with the present invention;

FIG. 13 is a schematic block diagram of another example of overlappingwrite requests and read requests for a set of encoded data slices inaccordance with the present invention;

FIG. 14 is a schematic block diagram of another example of overlappingwrite requests for a set of encoded data slices in accordance with thepresent invention;

FIG. 15 is a schematic block diagram of overlapping data accesstransactions in a dispersed storage network (DSN). in accordance withthe present invention; and

FIG. 16 is a logic flow diagram of an example of resolving overlappingdata access transactions. in accordance with the present invention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a schematic block diagram of an embodiment of a dispersed, ordistributed, storage network (DSN) 10 that includes a plurality ofcomputing devices 12-16, a managing unit 18, an integrity processingunit 20, and a DSN memory 22. The components of the DSN 10 are coupledto a network 24, which may include one or more wireless and/or wirelined communication systems; one or more non-public intranet systemsand/or public internet systems; and/or one or more local area networks(LAN) and/or wide area networks (WAN).

The DSN memory 22 includes a plurality of storage units 36 that may belocated at geographically different sites (e.g., one in Chicago, one inMilwaukee, etc.), at a common site, or a combination thereof. Forexample, if the DSN memory 22 includes eight storage units 36, eachstorage unit is located at a different site. As another example, if theDSN memory 22 includes eight storage units 36, all eight storage unitsare located at the same site. As yet another example, if the DSN memory22 includes eight storage units 36, a first pair of storage units are ata first common site, a second pair of storage units are at a secondcommon site, a third pair of storage units are at a third common site,and a fourth pair of storage units are at a fourth common site. Notethat a DSN memory 22 may include more or less than eight storage units36. Further note that each storage unit 36 includes a computing core (asshown in FIG. 2, or components thereof) and a plurality of memorydevices for storing dispersed error encoded data.

Each of the computing devices 12-16, the managing unit 18, and theintegrity processing unit 20 include a computing core 26, which includesnetwork interfaces 30-33. Computing devices 12-16 may each be a portablecomputing device and/or a fixed computing device. A portable computingdevice may be a social networking device, a gaming device, a cell phone,a smart phone, a digital assistant, a digital music player, a digitalvideo player, a laptop computer, a handheld computer, a tablet, a videogame controller, and/or any other portable device that includes acomputing core. A fixed computing device may be a computer (PC), acomputer server, a cable set-top box, a satellite receiver, a televisionset, a printer, a fax machine, home entertainment equipment, a videogame console, and/or any type of home or office computing equipment.Note that each of the managing unit 18 and the integrity processing unit20 may be separate computing devices, may be a common computing device,and/or may be integrated into one or more of the computing devices 12-16and/or into one or more of the storage units 36.

Each interface 30, 32, and 33 includes software and hardware to supportone or more communication links via the network 24 indirectly and/ordirectly. For example, interface 30 supports a communication link (e.g.,wired, wireless, direct, via a LAN, via the network 24, etc.) betweencomputing devices 14 and 16. As another example, interface 32 supportscommunication links (e.g., a wired connection, a wireless connection, aLAN connection, and/or any other type of connection to/from the network24) between computing devices 12 & 16 and the DSN memory 22. As yetanother example, interface 33 supports a communication link for each ofthe managing unit 18 and the integrity processing unit 20 to the network24.

Computing devices 12 and 16 include a dispersed storage (DS) clientmodule 34, which enables the computing device to dispersed storage errorencode and decode data as subsequently described with reference to oneor more of FIGS. 3-8. In this example embodiment, computing device 16functions as a dispersed storage processing agent for computing device14. In this role, computing device 16 dispersed storage error encodesand decodes data (e.g., data 40) on behalf of computing device 14. Withthe use of dispersed storage error encoding and decoding, the DSN 10 istolerant of a significant number of storage unit failures (the number offailures is based on parameters of the dispersed storage error encodingfunction) without loss of data and without the need for a redundant orbackup copies of the data. Further, the DSN 10 stores data for anindefinite period of time without data loss and in a secure manner(e.g., the system is very resistant to unauthorized attempts ataccessing the data).

In operation, the managing unit 18 performs DS management services. Forexample, the managing unit 18 establishes distributed data storageparameters (e.g., vault creation, distributed storage parameters,security parameters, billing information, user profile information,etc.) for computing devices 12-14 individually or as part of a group ofuser devices. As a specific example, the managing unit 18 coordinatescreation of a vault (e.g., a virtual memory block associated with aportion of an overall namespace of the DSN) within the DSTN memory 22for a user device, a group of devices, or for public access andestablishes per vault dispersed storage (DS) error encoding parametersfor a vault. The managing unit 18 facilitates storage of DS errorencoding parameters for each vault by updating registry information ofthe DSN 10, where the registry information may be stored in the DSNmemory 22, a computing device 12-16, the managing unit 18, and/or theintegrity processing unit 20.

The DSN managing unit 18 creates and stores user profile information(e.g., an access control list (ACL)) in local memory and/or withinmemory of the DSN memory 22. The user profile information includesauthentication information, permissions, and/or the security parameters.The security parameters may include encryption/decryption scheme, one ormore encryption keys, key generation scheme, and/or dataencoding/decoding scheme.

The DSN managing unit 18 creates billing information for a particularuser, a user group, a vault access, public vault access, etc. Forinstance, the DSTN managing unit 18 tracks the number of times a useraccesses a non-public vault and/or public vaults, which can be used togenerate a per-access billing information. In another instance, the DSTNmanaging unit 18 tracks the amount of data stored and/or retrieved by auser device and/or a user group, which can be used to generate aper-data-amount billing information.

As another example, the managing unit 18 performs network operations,network administration, and/or network maintenance. Network operationsincludes authenticating user data allocation requests (e.g., read and/orwrite requests), managing creation of vaults, establishingauthentication credentials for user devices, adding/deleting components(e.g., user devices, storage units, and/or computing devices with a DSclient module 34) to/from the DSN 10, and/or establishing authenticationcredentials for the storage units 36. Network administration includesmonitoring devices and/or units for failures, maintaining vaultinformation, determining device and/or unit activation status,determining device and/or unit loading, and/or determining any othersystem level operation that affects the performance level of the DSN 10.Network maintenance includes facilitating replacing, upgrading,repairing, and/or expanding a device and/or unit of the DSN 10.

The integrity processing unit 20 performs rebuilding of ‘bad’ or missingencoded data slices. At a high level, the integrity processing unit 20performs rebuilding by periodically attempting to retrieve/list encodeddata slices, and/or slice names of the encoded data slices, from the DSNmemory 22. For retrieved encoded slices, they are checked for errors dueto data corruption, outdated version, etc. If a slice includes an error,it is flagged as a ‘bad’ slice. For encoded data slices that were notreceived and/or not listed, they are flagged as missing slices. Badand/or missing slices are subsequently rebuilt using other retrievedencoded data slices that are deemed to be good slices to produce rebuiltslices. The rebuilt slices are stored in the DSTN memory 22.

FIG. 2 is a schematic block diagram of an embodiment of a computing core26 that includes a processing module 50, a memory controller 52, mainmemory 54, a video graphics processing unit 55, an input/output (IO)controller 56, a peripheral component interconnect (PCI) interface 58,an IO interface module 60, at least one IO device interface module 62, aread only memory (ROM) basic input output system (BIOS) 64, and one ormore memory interface modules. The one or more memory interfacemodule(s) includes one or more of a universal serial bus (USB) interfacemodule 66, a host bus adapter (HBA) interface module 68, a networkinterface module 70, a flash interface module 72, a hard drive interfacemodule 74, and a DSN interface module 76.

The DSN interface module 76 functions to mimic a conventional operatingsystem (OS) file system interface (e.g., network file system (NFS),flash file system (FFS), disk file system (DFS), file transfer protocol(FTP), web-based distributed authoring and versioning (WebDAV), etc.)and/or a block memory interface (e.g., small computer system interface(SCSI), internet small computer system interface (iSCSI), etc.). The DSNinterface module 76 and/or the network interface module 70 may functionas one or more of the interface 30-33 of FIG. 1. Note that the 10 deviceinterface module 62 and/or the memory interface modules 66-76 may becollectively or individually referred to as IO ports.

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data. When a computing device 12 or 16 has data tostore it disperse storage error encodes the data in accordance with adispersed storage error encoding process based on dispersed storageerror encoding parameters. The dispersed storage error encodingparameters include an encoding function (e.g., information dispersalalgorithm, Reed-Solomon, Cauchy Reed-Solomon, systematic encoding,non-systematic encoding, on-line codes, etc.), a data segmentingprotocol (e.g., data segment size, fixed, variable, etc.), and per datasegment encoding values. The per data segment encoding values include atotal, or pillar width, number (T) of encoded data slices per encodingof a data segment i.e., in a set of encoded data slices); a decodethreshold number (D) of encoded data slices of a set of encoded dataslices that are needed to recover the data segment; a read thresholdnumber (R) of encoded data slices to indicate a number of encoded dataslices per set to be read from storage for decoding of the data segment;and/or a write threshold number (W) to indicate a number of encoded dataslices per set that must be accurately stored before the encoded datasegment is deemed to have been properly stored. The dispersed storageerror encoding parameters may further include slicing information (e.g.,the number of encoded data slices that will be created for each datasegment) and/or slice security information (e.g., per encoded data sliceencryption, compression, integrity checksum, etc.).

In the present example, Cauchy Reed-Solomon has been selected as theencoding function (a generic example is shown in FIG. 4 and a specificexample is shown in FIG. 5); the data segmenting protocol is to dividethe data object into fixed sized data segments; and the per data segmentencoding values include: a pillar width of 5, a decode threshold of 3, aread threshold of 4, and a write threshold of 4. In accordance with thedata segmenting protocol, the computing device 12 or 16 divides the data(e.g., a file (e.g., text, video, audio, etc.), a data object, or otherdata arrangement) into a plurality of fixed sized data segments (e.g., 1through Y of a fixed size in range of Kilo-bytes to Tera-bytes or more).The number of data segments created is dependent of the size of the dataand the data segmenting protocol.

The computing device 12 or 16 then disperse storage error encodes a datasegment using the selected encoding function (e.g., Cauchy Reed-Solomon)to produce a set of encoded data slices. FIG. 4 illustrates a genericCauchy Reed-Solomon encoding function, which includes an encoding matrix(EM), a data matrix (DM), and a coded matrix (CM). The size of theencoding matrix (EM) is dependent on the pillar width number (T) and thedecode threshold number (D) of selected per data segment encodingvalues. To produce the data matrix (DM), the data segment is dividedinto a plurality of data blocks and the data blocks are arranged into Dnumber of rows with Z data blocks per row. Note that Z is a function ofthe number of data blocks created from the data segment and the decodethreshold number (D). The coded matrix is produced by matrix multiplyingthe data matrix by the encoding matrix.

FIG. 5 illustrates a specific example of Cauchy Reed-Solomon encodingwith a pillar number (T) of five and decode threshold number of three.In this example, a first data segment is divided into twelve data blocks(D1-D12). The coded matrix includes five rows of coded data blocks,where the first row of X11-X14 corresponds to a first encoded data slice(EDS 1_1), the second row of X21-X24 corresponds to a second encodeddata slice (EDS 2_1), the third row of X31-X34 corresponds to a thirdencoded data slice (EDS 3_1), the fourth row of X41-X44 corresponds to afourth encoded data slice (EDS 4_1), and the fifth row of X51-X54corresponds to a fifth encoded data slice (EDS 5_1). Note that thesecond number of the EDS designation corresponds to the data segmentnumber.

Returning to the discussion of FIG. 3, the computing device also createsa slice name (SN) for each encoded data slice (EDS) in the set ofencoded data slices. A typical format for a slice name 60 is shown inFIG. 6. As shown, the slice name (SN) 60 includes a pillar number of theencoded data slice (e.g., one of 1-T), a data segment number (e.g., oneof 1-Y), a vault identifier (ID), a data object identifier (ID), and mayfurther include revision level information of the encoded data slices.The slice name functions as, at least part of, a DSN address for theencoded data slice for storage and retrieval from the DSN memory 22.

As a result of encoding, the computing device 12 or 16 produces aplurality of sets of encoded data slices, which are provided with theirrespective slice names to the storage units for storage. As shown, thefirst set of encoded data slices includes EDS 1_1 through EDS 5_1 andthe first set of slice names includes SN 1_1 through SN 5_1 and the lastset of encoded data slices includes EDS 1_Y through EDS 5_Y and the lastset of slice names includes SN 1_Y through SN 5_Y.

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of a data object that was dispersed storage error encodedand stored in the example of FIG. 4. In this example, the computingdevice 12 or 16 retrieves from the storage units at least the decodethreshold number of encoded data slices per data segment. As a specificexample, the computing device retrieves a read threshold number ofencoded data slices.

To recover a data segment from a decode threshold number of encoded dataslices, the computing device uses a decoding function as shown in FIG.8. As shown, the decoding function is essentially an inverse of theencoding function of FIG. 4. The coded matrix includes a decodethreshold number of rows (e.g., three in this example) and the decodingmatrix in an inversion of the encoding matrix that includes thecorresponding rows of the coded matrix. For example, if the coded matrixincludes rows 1, 2, and 4, the encoding matrix is reduced to rows 1, 2,and 4, and then inverted to produce the decoding matrix.

FIGS. 9A and 9B are schematic block diagrams of examples of a storagerevision indicator 94 and an anticipated storage revision indicator 94′,which support correct data access transaction behavior for an encodeddata slice(s). Both the storage revision indicator 94 and theanticipated storage revision indicator 94′ include a content revisionfield, a delete counter field and a contest counter field. The contentrevision field contains information that uniquely identifies the contentof an encoded data slice (e.g., hash thereof, revision level, etc.). Thedelete counter field contains information that indicates a number oftimes the encoded data slice has been deleted. The contest counter fieldcontains information that indicates a number of contests the encodeddata slice has participated in. As discussed below, the storage revisionindicator 94 supports resolution of data access contention of datastored or to be stored in the DSN.

FIG. 10 is a schematic block diagram of an example of overlapping writerequests 96, 100 and read requests 102 for a set of encoded data slicesin a dispersed storage network (DSN). The DSN includes computing devicesA, B and C 12 or 16, the network 24 of FIG. 1, and a set of storageunits 36. The computing devices 12 or 16 may be implemented by adispersed storage (DS) processing unit. For example, computing device Amay be implemented by a DS processing unit #1, computing device B may beimplemented by a DS processing unit #2, computing device C may beimplemented by a DS processing unit #3. Each storage unit of the set ofstorage units 36 includes one or both of a list of pending transactions92 (e.g., list of pending transactions 92-1 through 92-5) and a storagerevision indicator 94 (e.g., storage revision indicators 94-1 through94-5). Each computing device may generate and store a set of anticipatedstorage revision indicators 94-1′-94-5′ related to a data accesstransaction (e.g., a write request 96 or 100).

In an example of operation, conflicting write requests are sent to thestorage units 36 for a set of encoded data slices 98 having the same setof slice names. Conflicting write requests occur when two or more DSprocessing units issue write requests for the same set of encoded dataslices at substantially the same time. In this situation, latency ofcommunication between the DS processing unit and the storage units is acontributing factor in determining, which, if any, of the DS processingunits “win” the conflict.

As a specific example, computing devices A and B (e.g., computing device12 or 16 of FIG. 1) issue conflicting write requests regarding a set ofencoded data slices with the same set of slices names to the storageunits (SU 1-5). As shown in FIG. 11, a write request includes atransaction number field, a slice name (SN) field, an encoded data slice(EDS) field, a current revision level field, and a new revision levelfield. Each write request in the set of write requests includes the sametransaction number, a different slice name, a different EDS, the samecurrent revision level, and the same new revision level.

Returning to the discussion of FIG. 10, computing device C issues aconflicting read request for the same set of encoded data slices (e.g.,issued at substantially the same time as the write requests fromcomputing devices A and B). As shown in FIG. 12, a read request 102includes a transaction number field, a slice name (SN) field, and acurrent revision level field. Each read request in the set of readrequests 102 includes the same transaction number, a different slicename, and the same current revision level.

FIG. 13 further illustrates the content of the conflicting sets of writerequests and the set of read requests for a set of encoded data sliceshaving the same set of slice names and current revision level. In thisexample, each of computing devices A and B disperse storage error encodea data segment into a set of five encoded data slices and generates fivewrite requests 96-1 through 96-5 and 100-1 through 100-5, respectively.The write requests from computing device A include the same transactionnumber of 0413 (which may be randomly generated, may be a time stamp,etc.), slice names (SN 1_1 through SN 5_1), encoded data slices (EDSA_1_1 through EDS A_5_1), the same current revision level of 003, andthe same next revision level of 004.

The write requests form computing device B include the same transactionnumber of 0279, slice names (SN 1_1 through SN 5_1), encoded data slices(EDS B_1_1 through EDS B_5_1), the same current revision level of 003,and the same next revision level of 004. A comparison of the writerequests from computing device A with the write requests from computingdevice B yields that the write requests have the same slice names, thesame current revision levels, and the same next revision levels. Thewrite requests differ in the transaction numbers and in the encoded dataslices. In addition, computing device C issues five read requests 102-1through 102-5 for the set of encoded data slices 98. The read requestsinclude the same transaction number of 0338, different slice names (SN1_1 through SN 5_1), and the current revision level of 003.

Returning to the discussion of FIG. 10, as each storage unit receivesits respective write requests (e.g., one from each of computing devicesA and B), it generates and stores one or both of a list of pendingtransactions 92 (e.g., 92-1 by SU #1) and a storage revision indicator94 (e.g., 94-5 by SU #5). The list of pending transactions 92 include atime ordered list of transaction numbers, or other indication,associated with data access requests regarding the slice names that werereceived for the conflicting write requests. The storage revisionindicators 94 include a content revision field, a delete counter field,and a contest counter field.

As a specific example, a first write request from computing device Aregarding a version of an encoded data slice having the first slice namehas a first transaction number (e.g., 0413) and a second write requestfrom computing device B regarding another version of the encoded dataslice having the first slice name has a second transaction number (e.g.,0279). Storage unit #1 received the first write request before receivingthe second write request, as such the first write request (e.g., thefirst transaction number) in a first priority position and the secondwrite request in a second priority position.

As another specific example, a write request from computing device Aregarding a version of an encoded data slice having a second slice namehas the first transaction number (e.g., 0413) and a write request fromcomputing device B regarding another version of the encoded data slicehaving the second slice name has the second transaction number (e.g.,0279). Storage unit #2 received the write request from computing deviceB before receiving the write request from computing device A. As such,the write request of computing device B (e.g., the second transactionnumber) in the first priority position and the write request fromcomputing device A in a second priority position. The remaining storageunits generate their respective list of pending transactions in asimilar manner.

After generating the list of pending transactions 92 and the storagerevision indicators 94, the storage units send back write responses tothe respective computing devices 12 or 16. The computing deviceinterprets the write responses to determine whether a threshold number,or more, (e.g., decode threshold number, write threshold number, etc.)of its write requests is in the first priority position or its storagerevision indicators match anticipated storage revision indicators. Whenthere is not an overlapping write request, the write requests will be inthe first priority position and the storage revision indicator willmatch anticipated storage revision indicators. As such, the computingdevice sends finalize requests to the storage units.

The storage units process the finalize request to make the new versionof the encoded data slices as the most recent set of encoded dataslices. When there is an overlapping write request, the write requestswill be in the second priority position and the storage revisionindicators will not match anticipated storage revision indicators. Assuch, the computing device may issue a rollback request. As thecomputing devices receive the list of pending transactions and storagerevision indicators, it determines whether at least the threshold numberof their respective write requests are in first priority position orwhether the storage revision indicators match anticipated storagerevision indicators. If yes, the computing device issues the finalizecommands. If not, the computing device withdraws it write requests orexecutes some other fallback position.

As another specific example, computing device A and B 12 or 16 sendoverlapping write requests 96 and 100 to the set of storage units 36.Storage units 1, 3, 4 and 5 receive write requests 96 first and storageunit 2 receives write request 100 first. When determining how toproperly process overlapping write requests using storage revisionindicators 94, the computing device A receives write responses from theset of storage units 36, which include storage revision indicators 94.The computing device A then compares the received storage revisionindicators with anticipated storage revision indicators. In thisexample, the computing device A determines that for storage unit #1, thestorage revision indicator (e.g., the content revision field=rev. Y+1, ahash of the encoded data slice=a55b82, etc.) substantially matches ananticipated storage revision indicator (e.g., the content revisionfield=rev. Y+1, a hash of the encoded data slice=a55b82, etc.). Thecomputing device A processes write responses from storage units 3-5 insimilar manner to processing write responses from storage unit 1.

For storage unit #2, the computing device A determines that the storagerevision indicator (e.g., the content revision field=rev. Y+2, a hash ofthe encoded data slice=bb5x8c, etc.) does not substantially match ananticipated storage revision indicator (e.g., the content revisionfield=rev. Y+1, a hash of the encoded data slice=a55b82, etc.). Thecontent revision field of the storage revision indicator received fromstorage unit #2 does not match the content revision field of theanticipated storage revision indicator due to the write request 100 fromcomputing device B being processed before the write request 96 fromcomputing device A. As such, the computing device A may issue to storageunit #2 a rollback request, may withdraw its write request or mayexecute some other fallback position. Computing device B also determineshow to properly process the overlapping write requests using storagerevision indicators 94 in similar manner to computing device A.

In addition to the two write requests, computing device C is sendingread requests to the storage units for the set of encoded data slices98. The storage units add the read requests to their respective list ofpending transactions and sends the updated list of pending transactionsto computing device C. Upon receiving the list of pending transactions,computing device C determines whether to proceed with the read request(e.g., read the current revision level of the set of encoded dataslices) or terminate the read request. As an alternative, computingdevice C processes the list of pending transactions to determine thatthe new set of encoded data slices from computing device A or computingdevice B will be the next current version of the set of encoded dataslices. Having made this determination, computing device C modifies itsread requests to read the next current version of the set of encodeddata slices.

Note the dispersed storage network may also include a priority protocol,which determines which one of and how overlapping data accesstransactions are to be processed. For example, in a write-deleteconflict, the priority protocol may indicate that the delete requestoverrides the write request. As another example, the priority protocolmay indicate that a write request from a higher ranked entity overridesa delete request from a lower ranked entity. As a yet further example,the priority protocol may indicate that when neither conflicting writerequest has a threshold number of favorable storage revision indicatormatches, that both write requests should not be processed, oralternatively, only one of the write requests should be re-tried.Alternatively, the priority protocol may indicate that the computingdevice with more successful higher priority requests override a numberof the other computing devices' successful lower priority requests. Notethe number may be a difference between the number of successful higherpriority requests and the threshold number.

FIG. 14 is a schematic block diagram of another example of overlappingwrite requests for a set of encoded data slices. In this example, whilethe write requests 96 and 100 and the read requests 102 are sent out atsimilar times, due to differing latencies and/or processing capabilitiesbetween the computing devices and storage units, the requests arereceived at different times and, potentially in a different order, bythe storage units than the order in which they were transmitted.

Prior to the reception of any of the read or write requests, the storageunits store a current revision level of the set of encoded data slices.As shown, storage unit SU#1 stores EDS 1_1, storage unit SU#2 stores EDS2_1, and so on. In this example, the current revision level of theencoded data slices is 003. In addition, each of the storage unitsinclude a storage revision indicator for their respective encoded dataslice.

In this example, when a storage unit receives a data access request, thestorage unit accesses the storage revision indicator regarding the dataaccess transaction it just received, updates information within thestorage revision indicator based on the type of data access transaction,and sends the updated storage revision indicator to the computing devicefrom which it received the request.

For example, each of storage units 1, 3, 4, and 5 received the writerequest from computing device A first. Accordingly, each storage unit1-5 updates its storage revision indicators (e.g., 94-1-1, 94-2-2,94-3-1, 94-4-1, and 94-5-1) for write requests from computing device A,which are then sent as write responses back to computing device A.Continuing with the example, storage unit #2 receives the write requestfrom computing device B first. Accordingly, each storage unit 1-5updates its storage revision indicator (e.g., 94-1-2, 94-2-2, 94-3-2,94-4-2, and 94-5-2) for write requests from computing device B, whichare then sent as write responses to computing device B.

After receiving the write responses from the storage units, thecomputing devices compare each storage revision indicator with eachrespective storage revision indicator. The computing devices A and Bprocess the data access transactions as previously discussed and/or asdescribed with reference to FIGS. 15 and 16.

FIG. 15 is a schematic block diagram of overlapping data accesstransactions in a dispersed storage network (DSN). The DSN includescomputing devices 12 or 16, the network 24 of FIG. 1, and a set ofstorage units 36. The storage units store object A (rev. x) as encodeddata slices (e.g., EDS 1_1_A through 5_1_A) and storage object B (rev.Y) as encoded data slices (e.g., EDS 1_1_B through 5_1_B). The set ofstorage units also store revision indicators (SRIs) SRI 1_1_A-SRI 5_1_Awhich correspond to encoded data slices EDS 1_1_A through 5_1_A andstore revision indicators (SRIs) SRI 1_1_B-SRI 5_1_B which correspond toencoded data slices EDS 1_1_B through 5_1_B.

In an example of operation, computing device B 12 or 16 sends a dataaccess request (e.g., an ensure operation) for object B 97 to the set ofstorage units #1-#5 36. An ensure operation is a data access transactionthat guarantees one or more of a transaction's data match a specificrevision, but does not update the checked data to a new revision. Forexample, an ensure operation for the update of object B (e.g., requestfor object B 97) is only valid if data object A has not been changedfrom a previously read revision (e.g., rev. X).

The storage units access the storage revision indicators for data objectB (e.g., SRI 1_1_B-SRI 5_1_B) and the storage revision indicators fordata object A (e.g., SRI 1_1_A-SRI 5_1_A), update the storage revisionindicators 94 and send the updated storage revision indicators as writeresponses to computing device B 12 or 16. The computing device B 12 or16 compares the received storage revision indicators to anticipatedstorage revision indicators 94′ (e.g., the anticipated storage revisionindicators 94′ of FIG. 9B). For example, the computing device B 12 or 16compares storage revision indicators 94 (SRI 1_1_B through SRI 5_1_B) toanticipated storage revision indicators 94′ for EDSs 1_1_B through EDS5_1_B and compares SRI 1_1_A through SRI 5_1_A to anticipated storagerevision indicators for EDSs 1_1_A through EDS 5_1_A.

For an ensure operation, all received storage indicators for the dataobject being checked against (e.g., data object A) must matchanticipated storage indicators for data object A (SRI 1_1_A through SRI5_1_A), as the data access request for data object B is only valid ifdata object A has not been changed from a previously read revision. Whenthe operation is not valid (e.g., one or more SRIs does notsubstantially match respective anticipated SRIs), the computing device B12 or 16 may void the request, or retry the request in accordance with aretry protocol.

When the operation is valid, the computing device B determines whetherat least a threshold number (e.g., a decode threshold, a read threshold,a write threshold, a pillar width number, or some other level betweenthe decode threshold and the pillar width number) of received storagerevision indicators substantially match anticipated storage revisionindicators for EDSs 1_1_B through 5_1_B. When at least a thresholdnumber of received storage revision indicators SRI 1_1_B-SRI 5_1_Bsubstantially match respective anticipated storage revision indicators,the computing device B executes the ensure operation (e.g., by sendingfinalize write requests to the set of storage units) to update dataobject B (e.g., update EDS 1_1_B through 5_1_B to a next revision (e.g.,rev. Y+1).

In another example of operation, a data access request (e.g., an ensureoperation) for object B 97 overlaps a data access request (e.g., adelete request, a write request) for object A 95. In this example,computing device A determines there is an overlapping request when acontest counter field of one or more of the storage revision indicatorsdoes not match the contest counter field of a respective anticipatedstorage revision indicator, due to the ensure operation from computingdevice B incrementing it. Computing device B determines there is anoverlapping request when the content revision of a storage revisionindicator (SRI 1_1_A-SRI 5_1_A) does not match an anticipated storagerevision indicator relating to EDS 1_1_A-EDS 5_1_A. For example, storagerevision indicator SRI 2_1_A includes rev. Y+1 in the content revisionfield and the anticipated storage revision indicator for EDS 2_1_Aincludes rev. Y in the content revision field (due to storage unit 2receiving request for object B 97 after receiving request for object A95). The computing device A or B may then process the overlapping writerequests in similar manner discussed above or as discussed in FIG. 16.For example, the computing devices A and B 12 or 16 may access apriority protocol to determine how to process the overlapping writerequests 96 and 100 (i.e., the priority protocol may indicate that theensure operation overrides the write operation).

FIG. 16 is a logic flow diagram of an example of resolving overlappingdata access transactions. The method beings at step 150, where acomputing device of a dispersed storage network (DSN) sends a set ofdata access requests regarding a data access transaction involving a setof encoded data slices that is stored or is to be stored to a set ofstorage units of the DSN. Note a data segment of a data object isdispersed storage error encoded into the set of encoded data slices.Further note the data access transaction includes one of a writeoperation, a delete operation, and an ensure operation.

The method continues at step 152, where the computing device receivesfrom each of at least some storage units of the set of storage units, astorage-revision indicator. The storage-revision indicator includes acontent-revision field, a delete-counter field, and a contest-counterfield. The content-revision uniquely identifies content of an encodeddata slice of the set of encoded data slices, the delete-counterindicates a number of times the encoded data slice has been deleted, andthe contest-counter indicates a number of data access contests theencoded data slice has participated in.

The method continues at step 154, where the computing device generatesan anticipated storage-revision indicator for the data accesstransaction based on a current revision level of the set of encoded dataslices and based on a data access type of the data access transaction.The method continues at step 156, where the computing device comparesthe anticipated storage-revision indicator with the storage-revisionindicators received from the at least some storage units.

In an example of operation, the computing device (e.g., a DS processingunit) sends a set of data access requests regarding a data accesstransaction. During the data access transaction (e.g., a writeoperation, delete operation, ensure operation, etc.), the computingdevice generates an anticipated storage revision indicator. Thecomputing device generates the anticipated storage revision indicator byone or more of performing a look up (e.g., accessing a table), byreceiving a current storage revision indicator 94, etc., and then bymodifying information within the fields of the current storage revisionindicator 94 based on the data access transaction. The anticipatedstorage revision indicator includes the same fields as the storagerevision indicator, but contains modified (e.g., expected) informationin the fields based on the type of operation (e.g., data accesstransaction). For example, a write operation changes the contentrevision field (e.g., update from ver. 1 to ver. 2, hash from 84hhj to3sx9j, etc.), leaves the delete counter unchanged and resets the contestcounter to zero. A delete operation changes the content revision fieldto ‘null’, increments information in the delete counter field, andresets the contest counter field to zero. An ensure operation, leavesinformation the content revision field unchanged, leaves information inthe delete counter unchanged, and increments the contest counter.

When a threshold number of the storage-revision indicators received fromthe at least some storage units does not substantially match theanticipated storage-revision indicator, the method continues at step158, where the computing device by the DS processing unit, the dataaccess transaction in accordance with a retry protocol. The retryprotocol may raise priority of the data access transaction, the longerthe wait is to retry, based on the number of retries, and in accordancewith timeframe.

For example, the computing device receives storage revision indicators94 from each of the storage units and compares the anticipated storagerevision indicator with each storage revision indicator 94. When thecomputing device determines that a threshold number of the storagerevision indicators received from the at least some storage unitssubstantially match the anticipated storage revision indicator, thecomputing device executes the data access transaction. When thecomputing device determines that a threshold number of the storagerevision indicators received from the at least some storage units doesnot substantially match the anticipated storage revision indicator, thecomputing device re-tries the data access transaction in accordance witha retry protocol (e.g., a longer wait or more re-tries indicates highera priority, certain number of retries, etc.).

When a threshold number of the storage-revision indicators received fromthe at least some storage units substantially match the anticipatedstorage-revision indicator, the method continues at step 160, where thecomputing device executes the data access transaction.

It is noted that terminologies as may be used herein such as bit stream,stream, signal sequence, etc. (or their equivalents) have been usedinterchangeably to describe digital information whose contentcorresponds to any of a number of desired types (e.g., data, video,speech, audio, etc. any of which may generally be referred to as‘data’).

As may be used herein, the terms “substantially” and “approximately”provides an industry-accepted tolerance for its corresponding termand/or relativity between items. Such an industry-accepted toleranceranges from less than one percent to fifty percent and corresponds to,but is not limited to, component values, integrated circuit processvariations, temperature variations, rise and fall times, and/or thermalnoise. Such relativity between items ranges from a difference of a fewpercent to magnitude differences. As may also be used herein, theterm(s) “configured to”, “operably coupled to”, “coupled to”, and/or“coupling” includes direct coupling between items and/or indirectcoupling between items via an intervening item (e.g., an item includes,but is not limited to, a component, an element, a circuit, and/or amodule) where, for an example of indirect coupling, the intervening itemdoes not modify the information of a signal but may adjust its currentlevel, voltage level, and/or power level. As may further be used herein,inferred coupling (i.e., where one element is coupled to another elementby inference) includes direct and indirect coupling between two items inthe same manner as “coupled to”. As may even further be used herein, theterm “configured to”, “operable to”, “coupled to”, or “operably coupledto” indicates that an item includes one or more of power connections,input(s), output(s), etc., to perform, when activated, one or more itscorresponding functions and may further include inferred coupling to oneor more other items. As may still further be used herein, the term“associated with”, includes direct and/or indirect coupling of separateitems and/or one item being embedded within another item.

As may be used herein, the term “compares favorably”, indicates that acomparison between two or more items, signals, etc., provides a desiredrelationship. For example, when the desired relationship is that signal1 has a greater magnitude than signal 2, a favorable comparison may beachieved when the magnitude of signal 1 is greater than that of signal 2or when the magnitude of signal 2 is less than that of signal 1. As maybe used herein, the term “compares unfavorably”, indicates that acomparison between two or more items, signals, etc., fails to providethe desired relationship.

As may also be used herein, the terms “processing module”, “processingcircuit”, “processor”, and/or “processing unit” may be a singleprocessing device or a plurality of processing devices. Such aprocessing device may be a microprocessor, micro-controller, digitalsignal processor, microcomputer, central processing unit, fieldprogrammable gate array, programmable logic device, state machine, logiccircuitry, analog circuitry, digital circuitry, and/or any device thatmanipulates signals (analog and/or digital) based on hard coding of thecircuitry and/or operational instructions. The processing module,module, processing circuit, and/or processing unit may be, or furtherinclude, memory and/or an integrated memory element, which may be asingle memory device, a plurality of memory devices, and/or embeddedcircuitry of another processing module, module, processing circuit,and/or processing unit. Such a memory device may be a read-only memory,random access memory, volatile memory, non-volatile memory, staticmemory, dynamic memory, flash memory, cache memory, and/or any devicethat stores digital information. Note that if the processing module,module, processing circuit, and/or processing unit includes more thanone processing device, the processing devices may be centrally located(e.g., directly coupled together via a wired and/or wireless busstructure) or may be distributedly located (e.g., cloud computing viaindirect coupling via a local area network and/or a wide area network).Further note that if the processing module, module, processing circuit,and/or processing unit implements one or more of its functions via astate machine, analog circuitry, digital circuitry, and/or logiccircuitry, the memory and/or memory element storing the correspondingoperational instructions may be embedded within, or external to, thecircuitry comprising the state machine, analog circuitry, digitalcircuitry, and/or logic circuitry. Still further note that, the memoryelement may store, and the processing module, module, processingcircuit, and/or processing unit executes, hard coded and/or operationalinstructions corresponding to at least some of the steps and/orfunctions illustrated in one or more of the Figures. Such a memorydevice or memory element can be included in an article of manufacture.

One or more embodiments have been described above with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claims. Further, the boundariesof these functional building blocks have been arbitrarily defined forconvenience of description. Alternate boundaries could be defined aslong as the certain significant functions are appropriately performed.Similarly, flow diagram blocks may also have been arbitrarily definedherein to illustrate certain significant functionality.

To the extent used, the flow diagram block boundaries and sequence couldhave been defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claims. One of average skill in the art will alsorecognize that the functional building blocks, and other illustrativeblocks, modules and components herein, can be implemented as illustratedor by discrete components, application specific integrated circuits,processors executing appropriate software and the like or anycombination thereof.

In addition, a flow diagram may include a “start” and/or “continue”indication. The “start” and “continue” indications reflect that thesteps presented can optionally be incorporated in or otherwise used inconjunction with other routines. In this context, “start” indicates thebeginning of the first step presented and may be preceded by otheractivities not specifically shown. Further, the “continue” indicationreflects that the steps presented may be performed multiple times and/ormay be succeeded by other activities not specifically shown. Further,while a flow diagram indicates a particular ordering of steps, otherorderings are likewise possible provided that the principles ofcausality are maintained.

The one or more embodiments are used herein to illustrate one or moreaspects, one or more features, one or more concepts, and/or one or moreexamples. A physical embodiment of an apparatus, an article ofmanufacture, a machine, and/or of a process may include one or more ofthe aspects, features, concepts, examples, etc. described with referenceto one or more of the embodiments discussed herein. Further, from figureto figure, the embodiments may incorporate the same or similarly namedfunctions, steps, modules, etc. that may use the same or differentreference numbers and, as such, the functions, steps, modules, etc. maybe the same or similar functions, steps, modules, etc. or differentones.

Unless specifically stated to the contra, signals to, from, and/orbetween elements in a figure of any of the figures presented herein maybe analog or digital, continuous time or discrete time, and single-endedor differential. For instance, if a signal path is shown as asingle-ended path, it also represents a differential signal path.Similarly, if a signal path is shown as a differential path, it alsorepresents a single-ended signal path. While one or more particulararchitectures are described herein, other architectures can likewise beimplemented that use one or more data buses not expressly shown, directconnectivity between elements, and/or indirect coupling between otherelements as recognized by one of average skill in the art.

The term “module” is used in the description of one or more of theembodiments. A module implements one or more functions via a device suchas a processor or other processing device or other hardware that mayinclude or operate in association with a memory that stores operationalinstructions. A module may operate independently and/or in conjunctionwith software and/or firmware. As also used herein, a module may containone or more sub-modules, each of which may be one or more modules.

As may further be used herein, a computer readable memory includes oneor more memory elements. A memory element may be a separate memorydevice, multiple memory devices, or a set of memory locations within amemory device. Such a memory device may be a read-only memory, randomaccess memory, volatile memory, non-volatile memory, static memory,dynamic memory, flash memory, cache memory, and/or any device thatstores digital information. The memory device may be in a form a solidstate memory, a hard drive memory, cloud memory, thumb drive, servermemory, computing device memory, and/or other physical medium forstoring digital information.

While particular combinations of various functions and features of theone or more embodiments have been expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent disclosure is not limited by the particular examples disclosedherein and expressly incorporates these other combinations.

1. A method comprises: sending, by a dispersed storage (DS) processingunit of a dispersed storage network (DSN), a set of data access requeststo a set of storage units of the DSN, wherein the set of data accessrequests is regarding a data access transaction involving a set ofencoded data slices, wherein a data segment of a data object isdispersed storage error encoded into the set of encoded data slices, andwherein the set of storage units stores, or is to store, the set ofencoded data slices; receiving, by the DS processing unit from each ofat least some storage units of the set of storage units, astorage-revision indicator, wherein the storage-revision indicatorincludes a content-revision field, a delete-counter field, and acontest-counter field, wherein the content-revision uniquely identifiescontent of an encoded data slice of the set of encoded data slices,wherein the delete-counter indicates a number of times the encoded dataslice has been deleted, and wherein the contest-counter indicates anumber of data access contests the encoded data slice has participatedin; generating, by the DS processing unit, an anticipatedstorage-revision indicator for the data access transaction based on acurrent revision level of the set of encoded data slices and based on adata access type of the data access transaction; comparing, by the DSprocessing unit, the anticipated storage-revision indicator with thestorage-revision indicators received from the at least some storageunits; and when a threshold number of the storage-revision indicatorsreceived from the at least some storage units substantially match theanticipated storage-revision indicator, executing, by the DS processingunit, the data access transaction.
 2. The method of claim 1 furthercomprises: when a threshold number of the storage-revision indicatorsreceived from the at least some storage units does not substantiallymatch the anticipated storage-revision indicator, re-trying, by the DSprocessing unit, the data access transaction in accordance with a retryprotocol.
 3. The method of claim 1, wherein the data access transactioncomprises one of: a write operation; a delete operation; and an ensureoperation.
 4. The method of claim 1, wherein an ensure operationcomprises: verifying a revision level of a second data object prior toexecuting the data access transaction.
 5. The method of claim 1 furthercomprises: when the data access transaction is a write operation,generating the anticipated storage-revision indicator by: changinginformation in the content-revision field; leaving, unchanged,information in the delete-counter field; and resetting the contestcounter field to zero.
 6. The method of claim 1 further comprises: whenthe data access transaction is a delete operation, generating theanticipated storage-revision indicator by: resetting information in thecontent-revision field to null; incrementing information in thedelete-counter field; and resetting the contest counter field to zero.7. The method of claim 1 further comprises: when the data accesstransaction is an ensure operation, generating the anticipatedstorage-revision indicator by: leaving, unchanged, information in thecontent-revision field; leaving, unchanged, information in thedelete-counter field; and incrementing information in the contestcounter field.
 8. A dispersed storage (DS) processing unit of adispersed storage network (DSN), comprises: memory; an interface; and aprocessing module operably coupled to the memory and the interface,wherein the processing module is operable to: send a set of data accessrequests to a set of storage units of the DSN, wherein the set of dataaccess requests is regarding a data access transaction involving a setof encoded data slices, wherein a data segment of a data object isdispersed storage error encoded into the set of encoded data slices, andwherein the set of storage units stores, or is to store, the set ofencoded data slices; receive from each of at least some storage units ofthe set of storage units, a storage-revision indicator, wherein thestorage-revision indicator includes a content-revision field, adelete-counter field, and a contest-counter field, wherein thecontent-revision uniquely identifies content of an encoded data slice ofthe set of encoded data slices, wherein the delete-counter indicates anumber of times the encoded data slice has been deleted, and wherein thecontest-counter indicates a number of data access contests the encodeddata slice has participated in; generate an anticipated storage-revisionindicator for the data access transaction based on a current revisionlevel of the set of encoded data slices and based on a data access typeof the data access transaction; compare the anticipated storage-revisionindicator with the storage-revision indicators received from the atleast some storage units; and when a threshold number of thestorage-revision indicators received from the at least some storageunits substantially match the anticipated storage-revision indicator,execute the data access transaction.
 9. The DS processing unit of claim8, wherein the processing module is further operable to: when athreshold number of the storage-revision indicators received from the atleast some storage units does not substantially match the anticipatedstorage-revision indicator, re-try, by the DS processing unit, the dataaccess transaction in accordance with a retry protocol.
 10. The DSprocessing unit of claim 8, wherein the data access transactioncomprises one of: a write operation; a delete operation; and an ensureoperation.
 11. The DS processing unit of claim 8, wherein the processingmodule is further operable to perform an ensure operation by: verifyinga revision level of a second data object prior to executing the dataaccess transaction.
 12. The DS processing unit of claim 8, wherein theprocessing module is further operable to: when the data accesstransaction is a write operation, generate the anticipatedstorage-revision indicator by: changing information in thecontent-revision field; leaving, unchanged, information in thedelete-counter field; and resetting the contest counter field to zero.13. The DS processing unit of claim 8, wherein the processing module isfurther operable to: when the data access transaction is a deleteoperation, generate the anticipated storage-revision indicator by:resetting information in the content-revision field to null;incrementing information in the delete-counter field; and resetting thecontest counter field to zero.
 14. The DS processing unit of claim 8,wherein the processing module is further operable to: when the dataaccess transaction is an ensure operation, generate the anticipatedstorage-revision indicator by: leaving, unchanged, information in thecontent-revision field; leaving, unchanged, information in thedelete-counter field; and incrementing information in the contestcounter field.